| 基于多源遥感数据的纳板河国家级自然保护区人类活动用地监测 |
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| 引用本文： | 刘晓龙,徐瑞,付卓,史正涛,高书鹏.基于多源遥感数据的纳板河国家级自然保护区人类活动用地监测[J].农业工程学报,2018,34(19):266-275. |
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| 作者姓名： | 刘晓龙 徐瑞 付卓 史正涛 高书鹏 |
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| 作者单位： | 云南师范大学旅游与地理科学学院;环境保护部卫星环境应用中心 |
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| 基金项目： | 国家重点研发计划项目( 2016YFB0501404)；云南省青年基金“基于多源遥感数据的植被类型精细分类方法研究” (2016FD021)；高分对地观测重大专项（30-Y20A37-9003-15/17） |
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| 摘 要： | 人类活动对自然保护区内的珍稀及濒危野生动植物资源产生威胁。以自然保护区土地利用及其变化作为保护区内人类活动的代表。针对在低纬度热带地区多云雾天气对光学遥感成像产生严重干扰的条件下,如何实现基于多源遥感数据构建高时间、高空间分辨率遥感数据,监测复杂地形及气候环境下的热带雨林环境自然保护区土地利用变化监测,进而分析保护区人类活动这一问题展开研究。采用时空数据融合技术实现了2000年、2004年、2010年和2015年纳板河国家级自然保护区内人类活动(主要为人类活动用地:橡胶林、耕地、建筑用地)和自然地表(水体和自然林)分类识别,结果表明:1)时空数据融合技术能够实现复杂地形以及多云多雾天气条件下的高时空分辨率遥感数据,实现基于该时间序列数据的人类活动用地较高精度识别(2000年、2004年、2010年、2015年的总体分类精度分别为88.13%、86.88%、89.38%、90.63%,Kappa系数分别为0.834 0、0.817 6、0.853 3、0.871 1);2)纳板河国家级自然保护区内2000年至2015年期间,自然林的面积持续减少,橡胶林、耕地及建筑的面积持续增加;3)保护区内人类活动随地形的变化特征是:橡胶林及耕地范围在向坡度较大的地区扩张,大部分橡胶林种植在坡度为13?~24?之间,耕地也在向坡度较大的地区逐步扩张。该研究可为自然保护区监管部门及环境保护研究领域提供技术支持。
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| 关 键 词： | 遥感 土地利用 监测 纳板河国家级自然保护区 时空数据融合 时间序列 土地利用变化 |
| 收稿时间： | 2018/6/20 0:00:00 |
| 修稿时间： | 2018/9/3 0:00:00 |
Monitoring land use for human activities in Nabanhe National Nature Reserve based on multi-source remote sensing data |
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| Liu Xiaolong,Xu Rui,Fu Zhuo,Shi Zhengtao and Gao Shupeng.Monitoring land use for human activities in Nabanhe National Nature Reserve based on multi-source remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(19):266-275. |
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| Authors: | Liu Xiaolong Xu Rui Fu Zhuo Shi Zhengtao and Gao Shupeng |
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| Institution: | 1. College of Tourism & Geography Science, Yunnan Normal University, Kunming 650500, China;,1. College of Tourism & Geography Science, Yunnan Normal University, Kunming 650500, China;,2. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China,1. College of Tourism & Geography Science, Yunnan Normal University, Kunming 650500, China; and 1. College of Tourism & Geography Science, Yunnan Normal University, Kunming 650500, China; |
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| Abstract: | Abstract: Human activities within the Nature Reserves are considered a threat to the endangered species. This study takes the land cover/land use as the representative of human activities within the Nabanhe National Nature Reserve. As optical remote sensing images are frequently contaminated by cloud and frog, which will restrict its practicality in monitoring human activities in Nabanhe National Nature Reserve, this study aimed to fuse the multi-resources of optical remote sensing images to build a high spatio-temporal resolution data (30 m daily surface reflectance) for the year 2000, 2004, 2010 and 2015 using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The fused data was assessed during the data fusing procedure, and in a correlation of greater than 0.8 (with P<0.01) with the reference image for each period of time. The fused data was then used to generate the time-series NDVI (Normalized Difference Vegetation Index), which would be used to differentiate each of the 5 land covers (namely natural forest, rubber trees, water, farmland and built-up area) to be classified. Previous to the extraction of the time-series features, denoising of the time-series NDVI was conducted using the double S-G (Savitzky-Golay) filter. 6 features were generated using the denoised NDVI time-series data, and used to classify the 5 land covers above. The Random Forest classifier was used during the classification, and the RF classifier was trained using the reference samples that were selected from high spatial resolution Google Earth images. The overall accuracies of the final classification results were greater than 86.88%, with Kappa values greater than 0.817?6 (the overall classification accuracies for the year 2000, 2004, 2010 and 2015 were 88.13%, 86.88%, 89.38% and 90.63% respectively, and the corresponding Kappa values were 0.834?0, 0.817?6, 0.853?3 and 0.871?1, respectively). This accuracy guaranteed the availability of the classification results in monitoring human activities in Nabanhe National Nature Reserve. The land cover/land use changing trend was analyzed based on the classification results for each period of time, of which the results were as follows: from 2000 to 2004, water area increased mainly due to the conversion of farmland and built-up area, which occupied 0.06% and 0.01% respectively of the entire area of the Nabanhe National Nature Reserve. Farmland area increased, and the increased area was mainly from natural forest and rubber trees. Built-up area increased, and the increased area was from natural forest and farmland. The area of the increased rubber trees is the largest, and the increased rubber trees occupied natural forest and farmland. The only decreased land cover/land use was the natural forest during this period of time. From 2004 to 2010, water areas increased mainly due to the conversion of natural forest and rubber trees, because there was a hydropower station built during this period of time. Increased farmland area was mainly from forest, while increased built-up area was mainly from farmland. Rubber area was increasing due to the conversion of forest, the area of which was decreasing constantly during this period of time. From 2010 to 2015, increased water occupied farmland and the forest was changed to farmland with the largest area (96.47% of all land converted to farmland was forest). Expanded built-up area was from rubber farmland (occupied 76.92% of all land changed to built-up). It was during this time that the farmland changed the most, it was 5.29% of the area of the Nabanhe National Nature Reserve. Obvious land use changing trend corresponding to terrain was found from 2000 to 2015. Built-up distribution changed less, but the corresponding area increased. The rubber was distributed in areas with slopes ranging from 0 to 36 degrees, and it was first expanded from 0 to 12 degrees, and then to 24 degrees and now is distributed to near 36 degrees. The expansion of rubber is pushing the rubber planting to the limit in the Nabanhe National Nature Reserve. Same changing trend was found to the farmland from 2000 to 2015. Both of these land covers are expanding to steeper terrain and larger areas in the Nabanhe National Nature Reserve. The method provided by this study may support the governmental departments in monitoring human activities within Nature Reserves. |
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| Keywords: | remote sensing land use monitoring Nabanhe National Nature Reserve spatio-temporal data fusion time-series land cover/land use change |
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